---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: llama-le-out
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# llama-le-out

This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6239

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9364        | 0.06  | 100  | 0.8000          |
| 0.809         | 0.12  | 200  | 0.7724          |
| 0.8695        | 0.18  | 300  | 0.7571          |
| 0.7512        | 0.24  | 400  | 0.7406          |
| 0.8266        | 0.3   | 500  | 0.7327          |
| 0.7898        | 0.35  | 600  | 0.7238          |
| 0.9163        | 0.41  | 700  | 0.7135          |
| 0.6955        | 0.47  | 800  | 0.7025          |
| 0.7887        | 0.53  | 900  | 0.7009          |
| 0.7361        | 0.59  | 1000 | 0.6911          |
| 0.7736        | 0.65  | 1100 | 0.6897          |
| 0.7135        | 0.71  | 1200 | 0.6859          |
| 0.8138        | 0.77  | 1300 | 0.6788          |
| 0.7172        | 0.83  | 1400 | 0.6720          |
| 0.7387        | 0.89  | 1500 | 0.6695          |
| 0.7042        | 0.95  | 1600 | 0.6688          |
| 0.7231        | 1.0   | 1700 | 0.6652          |
| 0.7136        | 1.06  | 1800 | 0.6626          |
| 0.694         | 1.12  | 1900 | 0.6583          |
| 0.7401        | 1.18  | 2000 | 0.6551          |
| 0.63          | 1.24  | 2100 | 0.6519          |
| 0.6506        | 1.3   | 2200 | 0.6478          |
| 0.7436        | 1.36  | 2300 | 0.6457          |
| 0.5903        | 1.42  | 2400 | 0.6452          |
| 0.6861        | 1.48  | 2500 | 0.6399          |
| 0.6576        | 1.54  | 2600 | 0.6412          |
| 0.6327        | 1.59  | 2700 | 0.6357          |
| 0.6634        | 1.65  | 2800 | 0.6378          |
| 0.6419        | 1.71  | 2900 | 0.6349          |
| 0.6573        | 1.77  | 3000 | 0.6344          |
| 0.7052        | 1.83  | 3100 | 0.6327          |
| 0.6438        | 1.89  | 3200 | 0.6292          |
| 0.713         | 1.95  | 3300 | 0.6283          |
| 0.6357        | 2.01  | 3400 | 0.6293          |
| 0.5736        | 2.07  | 3500 | 0.6302          |
| 0.591         | 2.13  | 3600 | 0.6307          |
| 0.6995        | 2.19  | 3700 | 0.6295          |
| 0.6708        | 2.24  | 3800 | 0.6277          |
| 0.6329        | 2.3   | 3900 | 0.6262          |
| 0.6138        | 2.36  | 4000 | 0.6271          |
| 0.6316        | 2.42  | 4100 | 0.6266          |
| 0.6022        | 2.48  | 4200 | 0.6260          |
| 0.7221        | 2.54  | 4300 | 0.6252          |
| 0.6943        | 2.6   | 4400 | 0.6256          |
| 0.6616        | 2.66  | 4500 | 0.6246          |
| 0.6185        | 2.72  | 4600 | 0.6247          |
| 0.6417        | 2.78  | 4700 | 0.6239          |
| 0.6238        | 2.84  | 4800 | 0.6237          |
| 0.6024        | 2.89  | 4900 | 0.6236          |
| 0.6059        | 2.95  | 5000 | 0.6239          |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1